Method for detecting zonal underground target in mountain land based on ridge energy correction

ABSTRACT

The present invention discloses a method for detecting, recognizing, and positioning a zonal underground target in a mountain land environment by detecting a ridge position in the mountain land environment and carrying out energy correction. The method belongs to the interdisciplinary field of pattern recognition, remote sensing technology and terrain analysis. The zonal underground target can cause energy abnormity when the heat field thereof is different from that of a mountain mass, and the heat island effect of the ridge can also cause the energy of the mountain mass to be abnormal. However, the energy abnormity caused by the heat island effect is essentially different from the energy abnormity caused by the zonal underground target in the aspect of mode. Therefore, the present invention aims to achieve an effect of reducing a false alarm rate of detecting and recognizing a zonal underground target in the mountain land environment by eliminating the influence of the heat body effect generated by the ridge in the terrain on the weak energy abnormity mode presented by the zonal underground target. The present invention comprises steps of acquiring digital elevation information of terrain, performing de-noising pretreatment on the digital elevation information, detecting a ridge line, correcting energy at the ridge position, and detecting the zonal underground target.

TECHNICAL FIELD

The present invention belongs to the interdisciplinary field of patternrecognition, remote sensing technology, and terrain analysis, and inparticular, relates to a method for detecting a zonal underground targetin mountain land based on ridge heat radiation correction, where themethod improves the correct rate of detecting the underground target ina mountain land environment and meanwhile reduces a false alarm rate bypositioning a ridge position through terrain analysis and correctingridge heat radiation.

BACKGROUND

Generally, there are a large quantity of zonal targets in a mountainland environment, for example, underground rivers that exist in thenatural environment, and man-made underground petroleum pipelines, andrailway and road tunnels that pass through mountains. The undergroundrivers are important water resources on one hand, and on the other hand,need to be avoided during construction in mountain land. Therefore, howto accurately detect and position an underground river has greatsignificance for both our sustainable development and modernizationprogress. Road tunnels and railway tunnels can pass through mountains,which not only greatly shortens the road length and reduces time peoplespend on travel, but also saves a lot of manpower and material resourcesfor constructing winding mountain roads and railways. Moreover, forautomobiles, tunnels are much safer than winding mountain roads.However, once these man-made underground constructions in the mountainland environment become malfunctioning, it is difficult to detect theposition where the malfunction occurs. Therefore, to accurately detectand position these underground zonal targets has significant influenceon various aspects of people's transportation and life. Therefore, it isnecessary to carry out study on detection and positioning of a zonalunderground target in a mountain land environment with a relatively lowfalse alarm rate and a relatively high recognition rate.

Certainly, contact type artificial detection is the commonest and mostdirect method for detecting tunnel facilities. However, this method isvery time-consuming and needs a lot of manpower and material resources.Although infrared imaging is put forward as a new technology fordetecting zonal underground targets and is applied to detection ofshallow underground pipelines, the application of infrared imaging indetecting deeply buried zonal underground targets has not been reportedhome and abroad.

Soils and rocks absorb solar energy and generate heat, and the heat, inthe form of infrared radiation, is detected by an infrared sensor. Theheat field of the mountain mass generally includes a stable part and avariable part, where the variable part is the shallow mountain mass ofwhich the temperature changes drastically under the effect of sunlight,and the stable part includes the mountain mass below the shallowmountain mass and an underground target buried therein. The suncyclically heats the variable part of the mountain mass every day. Heatexchange between the stable part and the variable part inside themountain mass and exchange of heat generated by the underground targetitself and heat of the stable part finally cause a detectabletemperature difference between the temperature of the mountain mass andthe temperature of the buried target, and this temperature difference isthe physical basis for detecting the underground target.

The temperature and energy of a zonal underground target are differentfrom those of surrounding mountain mass media, and finally present ablurred Gaussian-like pulse mode (positive or negative) in the mountainmass after heat transmission and diffusion. However, due to the heatisland effect of the ridge, the energy field at the ridge position alsoconforms to the blurred Gaussian-like pulse mode (positive or negative),which causes interference to the detection of the zonal undergroundtarget in mountain land.

SUMMARY

In view of the defect that a method for detecting a zonal undergroundtarget in a mountain land environment by using a blurred Gaussian-likepulse mode (positive or negative) is usually accompanied with arelatively high false alarm rate due the influence of the heat islandeffect of the ridge, the present invention provides a method fordetecting and positioning a zonal underground target, in which a ridgeposition is determined by using a ridge line detection algorithm, ridgeheat radiation is corrected, and then the zonal underground target isdetected and positioned by using an energy image after correction,thereby solving the problem of the high false alarm rate caused by theridge in the mountain land environment. The method for detecting a zonaltarget in mountain land based on ridge heat radiation correction in thepresent invention mainly includes steps of:

(1) acquiring digital elevation information of terrain, includingsub-steps of:

(1.1) determining the longitudes and latitudes of coverage of thedigital elevation information:

Soil and air environment are homogeneous in a certain range. First ofall, we should determine the range of the terrain within which thedigital elevation information needs to be obtained, and determine theposition of the detected range, that is, longitude and latitudeinformation. Because longitude and latitude information of each pointfurther needs to be determined in the following step, we'd betterdetermine a standard rectangular region. Herein, only longitude andlatitude information of four vertices of the rectangle needs to bedetermined, respectively marked as Pt1 (longti1, lati1), Pt2 (longti2,lati2), Pt3 (longti3, lati3), and Pt4 (longti4, lati4).

(1.2) calculating a longitude-latitude array in the coverage:

The resolution of the longitude-latitude array needs to be the same asthat of an energy (infrared) image, so that the finally obtained digitalelevation information of the terrain can correspond to the energy(infrared) image, so as to detect the ridge position by using thedigital elevation information of the terrain and finally find a positionwhere the ridge is located on the corresponding energy (infrared) image.

(1.2.1) calculating the width and height of a terrain range:

calculating, according to the longitudes and latitudes of the fourvertices of the rectangle determined in (1.1), the width and height ofthe rectangular coverage by using a distance measuring tool provided byGoogle Earth, the calculated width and height being marked as width andheight in meters, and calculating the range of the longitudes andlatitudes.

(1.2.2) calculating the longitude-latitude array:

sampling at intervals of step meters according to a sampling interval ofstep meters, to respectively calculate the number of sampling points inthe vertical direction and the number of sampling points in thehorizontal direction, that is: height/step and width/step, andcalculating a longitude step long_step and a latitude step lati_stepbetween every two neighboring sampling points in the longitude-latitudearray, thereby calculating the longitude and latitude of each samplingpoint in the longitude-latitude array.

(1.3) calculating an elevation array by using Google Earth:

Google Earth provides a programming interface, which allows us to inputthe longitude-latitude array in (1.2.2) to Google Earth, to obtainelevation data of each sampling point; and a digital elevationinformation array of the terrain is generated according to the elevationdata and output.

(2) performing de-noising pretreatment on the digital elevationinformation, including sub-steps as below:

The digital elevation information array obtained in (1.3) may carrycertain noise, where the noise may be caused by inaccurate elevationdata or a higher or lower height of an entire block when Google Earthstitches images. The higher or lower height of the entire block does notaffect detection of the ridge line, and therefore, for individual noisecaused by inaccurate elevation data, we use a mean filtering method toperform de-noising pretreatment on the original digital elevationinformation of the terrain. That is, a mean value of elevationinformation in a certain local range, for example, in a k*kneighborhood, is used as an output. In this way, the influence of therandomly distributed noise can be eliminated. Assuming that an actualelevation value of the i^(th) sampling point in a neighborhood is h_(i),a noise error of the sampling point is Δh_(i), and a finally observedvalue is h+Δh_(i), a process of using a mean value of pixels in aneighborhood as an output is as follows:

$\begin{matrix}{{\frac{1}{n}{\sum\limits_{i = 1}^{n}\; ( {h_{i} + {\Delta \; h_{i}}} )}} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\; h_{i}}} + {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; {\Delta \; h_{i}}}}}} \\{{\approx {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; h_{i}}}}}\end{matrix}$

that is, as the noise is randomly distributed, average noise of multiplesampling points is 0, and in this way, an elevation map thatapproximates an actual condition can be obtained.

(3) detecting a ridge line, including sub-steps of:

(3.1) detecting a ridge line along the horizontal direction:

The ridge line along the horizontal direction is the ridge line along anx direction. Traversing is performed along the x direction, to comparean elevation value of each sampling point with elevation values ofsampling points in a certain range (for example, in a range of 5sampling points, that is, 50 meters) in a y direction, where if thesampling point has a maximum value in the y direction, it is consideredthat the point is a candidate point of the ridge line along thehorizontal direction; the certain range may be set in advance.

(3.2) detecting a ridge line along the vertical direction:

The ridge line along the vertical direction is the ridge line along a ydirection. Traversing is performed along the y direction, to compare anelevation value of each sampling point with elevation values of samplingpoints in a certain range (for example, in a range of 5 sampling points,that is, 50 meters) in an x direction, where if the sampling point has amaximum value in the x direction, it is considered that the point is acandidate point of the ridge line along the vertical direction; thecertain range may be set in advance.

(3.3) extracting a continuous ridge line:

A normal ridge line point should be continuous rather than beingisolate. However, ridge line candidate points extracted according tostep (3.1) and step (3.2) may be isolate points that are not continuous.Therefore, we should judge the continuity of each ridge line candidatepoint. A criterion for the judgment is as follows: if a total number ofridge line candidate points in a neighborhood t*t of the ridge linecandidate point is greater than th_num, the ridge line candidate pointis a final ridge line point; otherwise, it is judged that the isolateridge line candidate point is a non-ridge line point, where t is apreset value. In this way, a final continuous ridge line mark map can beobtained.

(4) correcting energy at the ridge position, including sub-steps of:

(4.1) analyzing an energy distribution feature at the ridge position:

The ridge position generally has the mountain mass effect, which mainlyrefers to the thermal effect of uplift land. The mountain mass createsthe surrounding climate, and at any given elevation, as a surface areaof the uplift land increases, the mountain mass has greater influence onitself and the surrounding environment. As an uplift heat island, themountain mass absorbs solar radiation and converts the radiation intolong-wave heat energy, and has a temperature much higher than freeatmosphere at the same elevation.

On the other hand, according to the analysis from the perspective of theheat transfer theory, heat always travels along the direction in whichconduction occurs most readily. The heat conductivity of rocks in themountain mass is 1.2 to 2.1 W/(m·° C.), while in comparison, the heatconductivity of external air in contact with the surface of the mountainmass is 0.024 W/(m·° C.). Therefore, when heat inside the mountain massmeets air, as the heat conductivity of the air is far less than the heatconductivity between rocks, most of the heat travels along the rocks andgathers at the ridge, generating such an energy distribution featurethat heat at the ridge is obviously higher.

(4.2) correcting energy at the ridge position:

The energy at the ridge position is corrected according to the energydistribution feature at the ridge position illustrated in (4.1), where aspecific correction method is: replacing energy of a sampling point onthe ridge line with a mean value of energy of sampling points on twosides of the ridge line. By correcting energy at the ridge position, thedetection and recognition false alarms can be effectively reduced.

(5) detecting the zonal underground target, including sub-steps of:

(5.1) setting parameters used for detecting, by means of traversing, thezonal underground target:

An infrared image of a mountain land region in which the zonalunderground target may exist is traversed to detect a position where ablurred Gaussian-like pulse mode (positive or negative) occurs, so as todetect a false alarm position while positioning the zonal undergroundtarget. Before the traversing and detection are started, the followingparameters need to be set:

Size of a sampling section and spacing from a comparison section to acentral section: suppose that a sampling section of a mountain landsurface under which the zonal underground target is assumed to exist hasa length of 1 pixels and a width of w pixels, and then sampling sectionsof mountain land surfaces on two sides of the zonal underground targetalso have the same length and width. A distance from the center ofeither of the sampling sections of the mountain land surfaces on twosides to the center of the sampling section above the zonal undergroundtarget is s pixels, where 1, w, and s are preset values.

Search direction: The direction of the zonal underground target in thisregion can be roughly estimated by looking up related data, and centralhead and tail coordinates P0 (x0, y0) and P1 (x1, y1) for traversing andsearching are set according to degree of the estimated direction, aslong as a line connecting the two points passes through a position nearthe midpoint of this region, because in this way, it is convenient tomove towards two sides for traversing and searching.

Distance for extension each time: Each time after one group of images ofthe mountain land surface under which the zonal underground target isassumed to exist and mountain land surfaces on two sides for comparisonis searched, shift a distance of d towards two sides to continue tosearch a new group. Searching is stopped automatically when imageboundaries are reached.

Pulse threshold: Only when absolute values of differences between anaverage gray value of each image sampling section, in the middle, of themountain land surface under which the zonal underground target isassumed to exist and average gray values of image sampling sections ofthe mountain land surfaces on two sides for comparison are both greaterthan the pulse threshold th, a pulse at this position is counted as avalid pulse; and if either of the two differences is less than the pulsethreshold, the pulse at this position is considered invalid due to aweak signal.

(5.2) Output a traversing and detection result:

moving the sampling section pixel by pixel from the point P0 (x0, y0) tothe point P1 (x1, y1) starting from the central head and tailcoordinates P0 (x0, y0) and P1 (x1, y1) for traversing and searchingdetermined in (5.1), and each time after the sampling section is movedby one pixel, shifting to the left and right by r pixels respectively,finding a position at which the middle section has a maximum differencewith the comparison sections on the left and right, and testing whethera pulse at this position is a valid pulse; if the pulse is a validpulse, increasing the number of valid pulses by one; then calculatingcoordinates P0′ and P1′ that are obtained after PO and P1 are shifted bythe distance for extension each time towards two sides, makingstatistics between P0′ and P1′ by using a sampling method the same asthat used between PO and P1, and counting the number of valid pulses,where a position determined by a group of terminal coordinates P0 andP1, between which the number of valid pulses accounts for a largestproportion in the total number of pulses, is the position of the zonalunderground target, and in the result, other positions where pulsesappear are false alarm positions.

The technical effect of the present invention is as follows: It is foundthrough study and experiments that in a mountain land environment, mostfalse alarms in zonal underground target detection appear at the ridgeposition, and simulation of a mountain mass temperature field alsoproves the existence of the ridge effect. Therefore, we provide a methodfor detecting a zonal underground target in mountain land based on ridgeheat radiation correction in which false alarms are reduced by detectingthe ridge position and correcting energy at the ridge position. Testresults show that this method can indeed significantly reduce falsealarms at the ridge position during detection of zonal undergroundtarget in mountain land, so that the detection result is more accurate.Moreover, the method is easy to implement, involves a small amount ofcalculation, and requires less parameters.

BRIEF DESCRIPTION

FIG. 1 is a schematic flow chart of a method for detecting a zonalunderground target in mountain land based on ridge heat radiationcorrection according to the present invention;

FIG. 2 is a schematic view of coverage of digital elevation informationof terrain in an embodiment of the present invention;

FIG. 3 is a view of digital elevation information of terrain acquired inan embodiment of the present invention;

FIG. 4 is a view showing a result after de-noising pretreatment isperformed on the digital elevation information of the terrain in anembodiment of the present invention;

FIG. 5 is a flow chart of a ridge line preliminary detection algorithmin an embodiment of the present invention;

FIG. 6 is a view showing a result of rigid line preliminary detection inan embodiment of the present invention;

FIG. 7 is a flow chart of a continuous ridge line extraction algorithmin an embodiment of the present invention;

FIG. 8 is a view showing a result of continuous ridge line extraction inan embodiment of the present invention;

FIG. 9 is a view showing a simulation result of temperature field of amountain mass in which no zonal underground target is located in anembodiment of the present invention;

FIG. 10 is a flow chart of a ridge heat radiation correction algorithmin an embodiment of the present invention;

FIG. 11 is an original infrared image obtained through simulation in anembodiment of the present invention;

FIG. 12 illustrates results of marking ridge positions on a simulatedinfrared image in an embodiment of the present invention;

FIG. 13 is a view showing a result of ridge heat radiation correction inan embodiment of the present invention;

FIG. 14 is a view with detection false alarm marks before ridge heatradiation correction in an embodiment of the present invention; and

FIG. 15 is a view with detection false alarm marks after ridge heatradiation correction in an embodiment of the present invention.

DETAILED DESCRIPTION

In the present invention, a zonal underground target used to illustratethe method for detecting and recognizing a zonal underground target in amountain land environment based on ridge heat radiation correction is atunnel in the mountain land environment, and an energy image of a regionwhere the tunnel is located is an infrared image that we obtain throughsimulation according to elevation information and infrared radiationcharacteristics of surface materials. When detection is performed byusing algorithms in the present invention, a same effect can be obtainedif the energy image mentioned in the present invention is replaced witha real infrared image.

The present invention provides, for the first time, a method fordetecting a zonal underground target in mountain land by using aninfrared imaging technology and multivariate information, aiming atsolving the problem of a high false alarm rate during detection of azonal underground target in mountain land by detecting a ridge positionand correcting energy at the ridge position.

Ridge position detection belongs to the field of terrain analysis, thatis, the position of the ridge line is automatically extracted by usingterrain information contained in terrain elevation data. A method ofextracting the ridge line from three-dimensional elevation data can beclassified into local algorithm and overall algorithm in principle. Inthe local algorithm, vertical and cross sections that form digitalelevation grids are analyzed to find a point with a maximum elevationvalue on the cross section, and the found point is used as a ridge linecandidate point; and then obtained candidate points are screened andsorted according to a certain rule, to obtain a required ridge line,where the cross section analysis method is a typical local algorithm.The overall algorithm is to simulate the state of natural running wateron the terrain surface, to find a watershed. However, in the localalgorithm, the overall change rule of the terrain cannot be estimated,and it is difficult to distinguish terrain noise when a ridge line isdetermined; therefore, the extracted ridge line candidate points have alot of noise, which causes inconvenience to subsequent ridge linedistinguishing, and even produces errors and makes it impossible tocarry out subsequent algorithms. The overall algorithm has strong noiseresistance, but requires a large amount of calculation, and the amountof calculation increases quadratically as the number of the digitalelevation grids increases.

The focus of the present invention is to achieve objectives of reducinga false alarm rate in zonal underground target detection and improving arecognition rate by detecting a ridge position and correcting energy atthe ridge position. The present invention provides a ridge detectionmethod with a small calculation amount and a high calculation speed, andon this basis, illustrates a ridge heat radiation correction method,thereby achieving the objective of accurately detecting and positioninga zonal underground target by using a blurred Gaussian-like pulse mode(positive or negative).

The present invention provides a method for detecting a zonalunderground target in a mountain land environment based on ridge heatradiation correction. As shown in FIG. 1, the method mainly includesfive steps of: (1) acquiring digital elevation information of terrain;(2) performing de-noising pretreatment on the digital elevationinformation; (3) detecting a ridge line; (4) correcting energy at theridge position; and (5) detecting the zonal underground target, toillustrate an execution process of algorithms thereof in detail:

(1) acquiring digital elevation information of terrain, includingsub-steps of:

(1.1) determining the longitudes and latitudes of coverage of thedigital elevation information:

The foregoing tunnel used as an example for description has a length of3000 meters. To cover the entire zonal underground target whileconsidering that the complexity of acquiring the digital elevationinformation of the terrain by using

Google Earth is in direct proportion to the area of a selected region,we finally determine the size of a region to be detected, and afterlooking up related data, we determine the position of the region to bedetected.

The specific position of the region to be detected, that is, longitudeand latitude information of four vertices P1, P2, P3, and P4 of theregion to be detected is as follows:

-   -   Pt1 (116.150049, 40.296833), Pt2 (116.0292983, 40.356959),    -   Pt3 (116.194775, 40.260787), Pt4 (115.970548, 40.311917); a        specific method for marking the four vertices is as shown in        FIG. 2.

(1.2) calculating a longitude-latitude array in the coverage:

The energy (infrared) image used in an example for illustration in thepresent invention is obtained through simulation according to theinfrared radiation characteristics, and the resolution of the image is10 meters. The resolution of the longitude-latitude array needs to bethe same as that of the energy (infrared) image, and therefore, asampling interval step of the longitude-latitude array herein is 10meters.

(1.2.1) calculating the width and height of the range of the terrain tobe detected:

calculating, according to the longitudes and latitudes of the fourvertices Pt1, Pt2, Pt3, and Pt4 of the rectangle determined in (1.1),the width and height of the rectangular coverage by using a distancemeasuring tool provided by Google Earth, where width=3800 meters, andheight=4000 meters.

(1.2.2) calculating a longitude-latitude array:

sampling at intervals of 10 meters according to a sampling interval, torespectively calculate the number of sampling points along the verticaldirection (latitudinal direction):

lati_num=height/step=4000/10=400, and

the number of sampling points along the horizontal direction(longitudinal direction):

long_num=width/step=3800/10 =380.

and calculate a longitude range:

$\begin{matrix}{{{long}_{—}{region}} = {{{longti}\; 2} - {{longti}\; 4}}} \\{= {116.0292983 - 115.970548}} \\{{= 0.0587503};}\end{matrix}$

and a latitude range

$\begin{matrix}{{{lati}_{—}{region}} = {{{lati}\; 2} - {{lati}\; 4}}} \\{= {40.356959 - 40.311917}} \\{{= 0.045042};}\end{matrix}$

in the longitude-latitude array, a longitude step between every twoneighboring sampling points

$\begin{matrix}{{{long}_{—}{step}} = {{long}_{—}{region}\text{/}{long}_{—}{num}}} \\{= {0.0587503\text{/}380}} \\{{= 0.0001546};}\end{matrix}$

and

a latitude step

$\begin{matrix}{{{lati}_{—}{step}} = {{lati}_{—}{region}\text{/}{lati}_{—}{num}}} \\{= {0.045042\text{/}400}} \\{= 0.000112605}\end{matrix}$

In this case, the longitude of a sampling point in the i^(th) row,j^(th) column of the longitude-latitude array locate is:

locate (i, j)_longt1=longti4+long_step*j;

the latitude of the sampling point in the i^(th) row, j^(th) column ofthe longitude-latitude array is:

locate (i, j)_lati=lati4+lati_step*(lati_num−i)

then, the longitude and latitude of the sampling point in the i^(th)row, j^(th) column of the longitude-latitude array is:

locate (i, j) (locate(i, j)_longti, locate(i, j)_lati).

(1.3) calculating an elevation array by using Google Earth:

Google Earth provides a programming interface. Coordinates of each thesampling point in the longitude-latitude array locate in (1.2.2) arearranged to form a vector row by row, that is, the (i+1)^(th) row oflocate is arranged after the i^(th) row to form a vector that is used asan input. Google Earth automatically reads longitude and latitude dataof each sampling point in sequence, and returns elevation datacorresponding to each sampling point. We only need to output thereturned elevation data vectors again in a form of an array thatincludes a total of lati num rows, where each row includes long_numsampling points. This array is the digital elevation information of theterrain. The digital elevation information of the terrain in thisembodiment is shown in FIG. 3.

(2) performing de-noising pretreatment on the digital elevationinformation, including sub-steps as below:

Because the digital elevation information array obtained in (1.3) hascertain noise, as shown in the area marked by the black rectangularframe in FIG. 2, and the noise may be caused by inaccurate elevationdata. Therefore, to eliminate the noise, de-noising pretreatment isperformed on the original digital elevation information of the terrainby using a mean filtering method. That is, the entire image istraversed, and the value of each sampling point is replaced with a meanvalue of elevation information in a certain local area, for example, inan s*s neighborhood. In this way, the influence of the randomlydistributed noise can be eliminated.

Illustration of the de-noising pretreatment algorithm: Assuming that anactual elevation value of the i^(th) sampling point in a neighborhood ish_(i), a noise error thereof is Δh_(i), and a finally observed value ish_(i)+Δh_(i), and a process of using a mean value of pixels in aneighborhood as an output is as follows:

$\begin{matrix}{{\frac{1}{n}{\sum\limits_{i = 1}^{n}\; ( {h_{i} + {\Delta \; h_{i}}} )}} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\; h_{i}}} + {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; {\Delta \; h_{i}}}}}} \\{{\approx {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; h_{i}}}}}\end{matrix}$

That is, because the noise is randomly distributed, average noise ofmultiple sampling points is 0, and in this way, an elevation map thatapproximates an actual condition can be obtained. In this embodiment,n=9, that is, s=3.

The result obtained after the de-noising pretreatment is performed onthe digital elevation information is as shown in FIG. 4, where the noisein the area marked by the black frame is eliminated.

(3) detecting a ridge line, including sub-steps as below:

An overall flow chart of a ridge line preliminary detection algorithm isshown in FIG. 5, which is specifically divided into two sub-steps: (3.1)detecting a ridge line along the horizontal direction and (3.2)detecting a ridge line along the vertical direction. After thepreliminary ridge line detection, elimination of a continuous ridge line(3.3) further needs to be performed.

(3.1) detecting a ridge line along the horizontal direction:

traversing the terrain digital elevation array row by row along thehorizontal direction, to compare an elevation value H (x, y) of eachsampling point with elevation values of sampling points in a certainrange (for example, in a range of 5 sampling points, that is, 50 meters)in the y direction, where if the sampling point has a maximum value inthe y direction, that is:

H(x, y)>H(x, y−5),

H(x, y)>H(x, y−4),

H(x, y)>H(x, y−3),

H(x, y)>H(x, y−2),

H(x, y)>H(x, y−1),

H(x, y)>H(x, y+1),

H(x, y)>H(x, y+2),

H(x, y)>H(x, y+3),

H(x, y)>H(x, y+4),

H(x, y)>H(x, y+5),

when the eight inequalities are all true, it is considered that thepoint is a ridge line candidate point in the horizontal direction, andin a ridge candidate point label array label, the point is set aslabel(x, y)=1; otherwise, the point is set as label(x, y)=0, indicatingthat the point is not a ridge line candidate point.

(3.2) detecting a ridge line along the horizontal direction:

traversing column by column along the vertical direction, to compare anelevation value of each sampling point with elevation values of samplingpoints in a certain range (for example, in a range of 5 sampling points,that is, 50 meters) in the x direction, where if the sampling point hasa maximum value in the x direction, that is:

H(x, y)>H(x−1, y),

H(x, y)>H(x−2, y),

H(x, y)>H(x−3, y),

H(x, y)>H(x−4, y),

H(x, y)>H(x−5, y),

H(x, y)>H(x+1, y),

H(x, y)>H(x+2, y),

H(x, y)>H(x+3, y),

H(x, y)>H(x+4, y),

H(x, y)>H(x+5, y),

when the eight inequalities are all true, it is considered that thepoint is a ridge line candidate point in the vertical direction, and inthe ridge candidate point label array label, the point is set aslabel(x, y)=1; otherwise, the point is set as label(x, y)=0, indicatingthat the point is not a ridge line candidate point.

A result of ridge line preliminary detection marked on the view of thedigital elevation information of the terrain, which is obtained after(3.1) and (3.2), is shown in FIG. 6.

(3.3) extracting a continuous ridge line:

Because the ridge line candidate points extracted according to steps(3.1) and (3.2) have false alarms, a continuous ridge line extractionalgorithm is provided, where a flow chart of the algorithm is shown inFIG. 7. For each ridge line candidate point in the ridge linepreliminary detection result, it is determined whether the ridge linecandidate point is a discontinuous isolate point or whether the numberof ridge line candidate points in a certain region (for example, in at*t neighborhood) of the ridge line candidate point is less than acertain threshold th num, and if yes, it is determined that the ridgeline candidate point is a non-ridge line point, to prevent false alarms.

In this embodiment, assuming that t=7 and th num=10, that is, if thetotal number of ridge line candidate points in a 7*7 neighborhood of theridge line candidate point is greater than 10, it is considered that theridge line candidate point is a ridge line point; otherwise, it isconsidered that the ridge line candidate point is a non-ridge linepoint. A result of continuous ridge line extraction marked on the viewof the digital elevation information of the terrain is shown in FIG. 8.

(4) correcting energy at the ridge position, including sub-steps of:

(4.1) analyzing an energy distribution feature at the ridge position:

Generally, the ridge position has a mountain mass effect: thetemperature at the ridge position is higher than the temperature at themountainside, which is proved herein by using a simulation result oftemperature field of a mountain mass in which no zonal undergroundtarget is located, as shown in FIG. 9. It can be seen that thetemperature at the ridge position at the center of the mountain mass ishigher than the temperature at the mountainside on two sides.

This phenomenon can also be analyzed from the perspective of the heattransfer theory: Data shows that the heat conductivity of rocks in themountain mass is 1.2 to 2.1 W/(m·° C.), while the heat conductivity ofexternal air in contact with the surface of the mountain mass is 0.024W/(m·° C.). Heat always travels along the direction in which conductionoccurs most readily. Therefore, when heat inside the mountain mass meetsair, as the heat conductivity of the air is far less than the heatconductivity between rocks, most of the heat travels along the rocks andgathers at the ridge, resulting in the phenomenon that the temperatureat the ridge is relatively high.

(4.2) correcting energy at the ridge position:

correcting energy at the ridge line position by means ofnearest-neighbor interpolation, where a flow chart of an algorithmthereof is shown in FIG. 10, which mainly includes steps of:

1. traversing to find ridge line points.

2. finding, for each ridge line point label(x, y), non-ridge line pointsin four neighborhoods thereof, that is, points whose value is 0 inlabel(x−1, y), label(x+1, y), label(x, y−1), and label(x, y+1).

3. calculating a mean value of energy corresponding to the non-ridgeline points in the four neighborhoods of the ridge line point label(x,y).

4. replacing energy of the ridge line point with the mean value of theenergy corresponding to the non-ridge line points in the fourneighborhoods, and finally obtaining an result after the ridge heatradiation correction. A simulated infrared image (energy image) withoutenergy correction is shown in FIG. 11. FIG. 12 shows a result in whichthe detected ridge positions are marked on the simulated infrared image.An infrared simulated image after energy correction at the ridge lineposition is shown in FIG. 13. By comparing FIG. 11 and FIG. 13, thebrightness of many bright positions is significantly reduced, and thepurpose of correcting energy at the ridge position is achieved. Thepossibility of false alarms at these positions during zonal undergroundtarget detection and recognition in step (5) will be reduced.

(5) detecting the zonal underground target, including sub-steps of:

(5.1) setting parameters used for detecting, by means of traversing, thezonal underground target:

the length and width of a sampling section: 1=25 and w=3;

a distance from centers of sampling sections on mountain land surfaceson two sides to the center of a sampling section above the tunnel: s=3pixels;

search direction: approximately 120°, and central head and tailcoordinates for traversing and searching: P0 (10, 27) and P1 (283,171);

distance for extension each time: d=13;

distance of left and right shifting for detection: r=3; and

pulse threshold: th=3; and

(5.2) outputting a traversing and detection result:

detecting the infrared simulated images before and after correction byusing the parameters set in (5.1) for detecting the zonal undergroundtarget, to obtain false alarm marks. As shown in FIG. 14 and FIG. 15,the false alarm rate decreases from 24.7% to 21%. As for the ridgeposition marked by the frame in FIG. 12, false alarms in FIG. 14 inwhich energy at the ridge is corrected are reduced compared with FIG. 3in which energy at the ridge is not corrected.

1. A method for detecting a zonal underground target in mountain landbased on ridge heat radiation correction, wherein the method comprisessteps of: (1) acquiring digital elevation information of terrain,comprising sub-steps of: (1.1) determining the longitudes and latitudesof coverage of the digital elevation information; (1.2) calculating alongitude-latitude array in the coverage of the digital elevationinformation according to the longitudes and latitudes; and (1.3)calculating the elevation of each point in the longitude-latitude arrayaccording to the longitude-latitude array, to obtain a digital elevationinformation array; (2) performing de-noising pretreatment on the digitalelevation information array obtained in step (1); (3) detecting a ridgeline according to the digital elevation information array on which thede-noising treatment has been performed, comprising sub-steps of: (3.1)detecting a ridge line along the horizontal direction; (3.2) detecting aridge line along the vertical direction; and (3.3) extracting acontinuous ridge line according to the ridge line detected along thehorizontal direction and the ridge line detected along the verticaldirection; (4) correcting energy at the ridge position: correctingenergy at the ridge position according to an energy distribution featureat the ridge position; and (5) detecting the zonal underground target inthe digital elevation information array, comprising sub-steps of: (5.1)setting parameters used for detecting, by means of traversing, the zonalunderground target; and (5.2) traversing the digital elevationinformation array according to the parameters to detect the zonalunderground target, and outputting the position of the detected zonalunderground target.
 2. The method of claim 1, wherein step (1.2)specifically comprises: (1.2.1) calculating the width and height of aterrain range: calculating, according to the longitudes and latitudes offour vertices of the rectangle determined in step (1.1), the width andheight of the rectangular coverage by using a distance measuring toolprovided by Google Earth, the calculated width and height being markedas width and height in meters, and calculating the range of thelongitudes and latitudes; (1.2.2) calculating the longitude-latitudearray: sampling at intervals of step meters according to a samplinginterval of step meters, to respectively calculate the number ofsampling points in the vertical direction and the number of samplingpoints in the horizontal direction, that is: height/step and width/step,and calculating a longitude step long_step and a latitude step lati_stepbetween every two neighboring sampling points in the longitude-latitudearray, thereby calculating the longitude and latitude of each samplingpoint in the longitude-latitude array.
 3. The method of claim 1, whereinstep (1.3) specifically comprises: inputting the longitude-latitudearray in (1.2.2) to Google Earth to obtain elevation data of eachsampling point, and generating the digital elevation information arrayof the terrain according to the elevation data and outputting thedigital elevation information array of the terrain.
 4. The method ofclaim 1, wherein step (2) is specifically: performing de-noisingpretreatment on the digital elevation information by using a meanfiltering method.
 5. The method of claim 1, wherein step (3.1) isspecifically: traversing along an x direction, to compare an elevationvalue of each sampling point with elevation values of sampling points ina preset range in a y direction, wherein if the sampling point has amaximum value in the y direction, the point is a candidate point of theridge line along the horizontal direction.
 6. The method of claim 1,wherein step (3.2) is specifically: traversing along a y direction, tocompare an elevation value of each sampling point with elevation valuesof sampling points in a preset range in an x direction, wherein if thesampling point has a maximum value in the x direction, the point is acandidate point of the ridge line along the vertical direction.
 7. Themethod of claim 1, wherein step (3.3) is specifically: judging thecontinuity of each ridge line candidate point extracted in step (3.1)and step (3.2), wherein a criterion for the judgment is as follows: ifthere are different ridge line candidate points in a neighborhood t*t ofthe ridge line candidate point, the ridge line candidate point is afinal ridge line point; otherwise, it is judged that the isolate ridgeline candidate point is a non-ridge line point, to finally obtain acontinuous ridge line mark map, wherein t is a preset value.
 8. Themethod of claim 1, wherein step (4) is specifically: correcting energyat the ridge position, and a specific correction method is: replacingenergy of a sampling point on the ridge line with a mean value of energyof sampling points on two sides of the ridge line.
 9. The method ofclaim 1, wherein the parameters set in step (5.1) are specifically: sizeof a sampling section and spacing from a comparison section to a centralsection: suppose that a sampling section of a mountain land surfaceunder which the zonal underground target is assumed to exist has alength of z pixels and a width of z pixels; sampling sections ofmountain land surfaces on two sides of the zonal underground target alsohave a length of z pixels and a width of z pixels; and a distance fromthe center of either of the sampling sections of the mountain landsurfaces on two sides to the center of the sampling section above thezonal underground target is s pixels, wherein z and s are preset values;search direction: estimating the direction of the zonal undergroundtarget in this region by looking up related data, and setting, accordingto degree of the estimated direction, central head and tail coordinatesP0 (x0, y0) and P1 (x1, y1) for traversing and searching, wherein a lineconnecting the two points passes through a position near the midpoint ofthis region; distance for extension each time: each time after searchingone group of images of the mountain land surface under which the zonalunderground target is assumed to exist and mountain land surfaces on twosides for comparison, shifting a distance of d towards two sides tocontinue to search a new group, wherein searching is stoppedautomatically when image boundaries are reached; and pulse threshold:only when absolute values of differences between an average gray valueof each image sampling section, in the middle, of the mountain landsurface under which the zonal underground target is assumed to exist andaverage gray values of image sampling sections of the mountain landsurfaces on two sides for comparison are both greater than the pulsethreshold th, a pulse at this position is counted as a valid pulse; andif either of the two differences is less than the pulse threshold, thepulse at this position is considered invalid due to a weak signal. 10.The method of claim 1, wherein step (5.2) is specifically: moving thesampling section pixel by pixel from the point P0 (x0, y0) to the pointP1 (x1, y1) starting from the central head and tail coordinates P0 (x0,y0) and P1 (x1, y1) for traversing and searching determined in (5.1),and each time after the sampling section is moved by one pixel, shiftingto the left and right by r pixels respectively, wherein r is a presetvalue, finding a position at which the middle section has a maximumdifference with the comparison sections on the left and right, andtesting whether a pulse at this position is a valid pulse; if the pulseis a valid pulse, increasing the number of valid pulses by one; thencalculating coordinates P0′ and P1′ that are obtained after P0 and P1are shifted by the distance for extension each time towards two sides,making statistics between P0′ and P1′ by using a sampling method thesame as that used between P0 and P1, and counting the number of validpulses, wherein a position determined by a group of terminal coordinatesP0 and P1, between which the number of valid pulses accounts for alargest proportion in the total number of pulses, is the position of thezonal underground target, and in the result, other positions wherepulses appear are false alarm positions.